Identification and Computational Analysis of Gene Regulatory Elements

Abstract

Over the last two decades, advances in experimental and computational technologies have greatly facilitated genomic research. Next-generation sequencing technologies have made de novo sequencing of large genomes affordable, and powerful computational approaches have enabled accurate annotations of genomic DNA sequences. Charting functional regions in genomes must account for not only the coding sequences, but also noncoding RNAs, repetitive elements, chromatin states, epigenetic modifications, and gene regulatory elements. A mix of comparative genomics, high-throughput biological experiments, and machine learning approaches has played a major role in this truly global effort. Here we describe some of these approaches and provide an account of our current understanding of the complex landscape of the human genome. We also present overviews of different publicly available, large-scale experimental data sets and computational tools, which we hope will prove beneficial for researchers working with large and complex genomes.

Principles and methods of in-silico prioritization of non-coding regulatory variants

“…Distal elements also show considerable tissue-specificity(Petretto 2006), rapid evolutionary turnover(Schmidt 2010), and little consensus in regulatory motifs; all of these characteristics make the identification and mapping of distal regulatory elements to their target genes extremely challenging. Nevertheless, more than half of the non-coding variants registered in the GWAS catalog are located at least 20kb from the nearest TSS, and thus are potentially involved in distal regulatory mechanisms(Taher 2015). As we increase general understanding of the complex 3D interactions between distal regulatory elements and their target genes, it will be important to develop advanced analytic approaches that integrate each of these rapidly evolving datasets.…”

“…However, the issue of how to best annotate and prioritize functionally relevant non-coding mutations still remains unresolved(Zhang 2015). While much of the human genome consists of introns, pseudo-genes, non-coding RNAs, repetitive segments, and loci with potentially regulatory elements, the precise roles of many of these sequences remain unknown(Taher 2015). In-silico data mining is often considered an inevitable prerequisite to laborious and expensive functional validation.…”

Identification and Validation of Novel Hedgehog-Responsive Enhancers Predicted by Computational Analysis of Ci/Gli Binding Site Density

“…Cluster regions that contained predicted Ci binding sites that mapped to exons or repeat regions were excluded. Repeat regions often have regulatory function [ 39 , 40 ]. However, testing the regulatory activity of Ci binding motifs in repetitive sequences, and the effect of their clustering in these regions, was beyond the scope of this study.…”

Expanded Insights Into Mechanisms of Gene Expression and Disease Related Disruptions

“…Taher et al ( 2015 ) noted that regulatory elements in non-protein coding RNA may be present in the genome as discrete elements or they may be clustered in locus control regions (LCRs). One important LCR that is well-studied, is located upstream of the beta globin- like genes.…”